The present invention relates to the field of Ultrasonic inspection, and more particularly to a method of reducing grain noise in ultrasonic waveform data sets by three-dimensional (3D) filtering.
Ultrasonic pulse-echo inspection of titanium and other large grain metal objects is plagued by grain noise produced by ultrasonic reflections from large grain interfaces. Grain noise occurs because the microstructure of metals such as titanium or the like can be coarse which causes the grains of the microstructure to return signals during a raster scan of the object with an ultrasonic transducer. Due to the extruding and forging processes used in forming titanium parts, grain structure, and therefore grain noise, can vary significantly between different regions of an object. Grain noise can typically vary between 6-20 dB. The grains of the microstructure can produce echo signals during scanning of an object that can mask or conceal flaw indications in a defective region or produce false indications of flaws in defect free regions. False flaw indications can result in a defect-free part being rejected for use in an application. Masked flaw indications can result in the unintentional use of a defective part. Obviously, either masked flaw indications or false flaw indications can result in significant waste of time and materials in the manufacture of metal parts and/or an undesirable increased risk of part failure.
Ultrasonic inspection is typically used to inspect large metal rotating parts such as billets prior to forging, sonic shapes after forging, and machined parts such as engine fan disks or the like.
Typically, flaws in metal parts have a smaller spatial extent than the ultrasound illuminated grains of the metal microstructure. Thus, depending on the calibration of an ultrasonic data acquisition system used in scanning the object, flaw or signal correlation in the waveform data set collected therewith can be significantly greater, temporally and spatially, than the temporal and spatial correlation of the grain noise in the data set.